Evolution of weedy giant ragweed (Ambrosia trifida): Multiple origins and gene expression variability facilitates weediness

Abstract Agricultural weeds may originate from wild populations, but the origination patterns and genetics underlying this transition remain largely unknown. Analysis of weedy‐wild paired populations from independent locations may provide evidence to identify key genetic variation contributing to this adaptive shift. We performed genetic variation and expression analyses on transcriptome data from 67 giant ragweed samples collected from different locations in Ohio, Iowa, and Minnesota and found geographically separated weedy populations likely originated independently from their adjacent wild populations, but subsequent spreading of weedy populations also occurred locally. By using eight closely related weedy‐wild paired populations, we identified thousands of unique transcripts in weedy populations that reflect shared or specific functions corresponding, respectively, to both convergently evolved and population‐specific weediness processes. In addition, differential expression of specific groups of genes was detected between weedy and wild giant ragweed populations using gene expression diversity and gene co‐expression network analyses. Our study suggests an integrated route of weedy giant ragweed origination, consisting of independent origination combined with the subsequent spreading of certain weedy populations, and provides several lines of evidence to support the hypothesis that gene expression variability plays a key role in the evolution of weedy species.

Agricultural practices result in dramatic changes to long-term selective pressures on standing variation for newly adaptive traits and novel mutations that improve fitness in new environments. Yet, rapid adaptation need not only reflect genetic changes within genes, because changes in gene expression can also be adaptive Lai et al., 2008;Mayrose et al., 2011). The recent, rapid emergence of several North American native dicots as major agricultural weeds in their native range (e.g., species of Ambrosia, Helianthus and Amaranthus) raises questions as to whether weedy populations tend to originate from multiple, independent wild populations or from a single origin, whether the transition to weediness across a species' range has a common genetic basis, and whether transcriptome changes hasten the evolution of weediness (Bock et al., 2020;Davis et al., 2015;Jhala et al., 2014;Leon et al., 2021;Regnier et al., 2016;Sauer, 1957;Tranel & Trucco, 2009;Ward et al., 2013;Waselkov et al., 2020).
Giant ragweed (Ambrosia trifida) is an annual dicot that grows in open, disturbed, and ruderal habitats and exhibits substantial phenotypic variability, both within and among populations. Its large population sizes and outcrossing mating system may also increase genetic variance and adaptation in response to natural selection.
Beyond sharing these characteristics with other North American native weeds, giant ragweed is well-suited to investigate the drivers of repeated invasion of crop fields by wild species because it is widely distributed among diverse early-successional habitats, yet varies geographically in its presence and severity as an agricultural weed (Regnier et al., 2016). Specifically, despite its presence as an important agricultural weed for decades in the Eastern Corn Belt, giant ragweed has only relatively recently spread to agricultural settings in the western part of its native range (Regnier et al., 2016). This variation in weediness, set against a near-continuous background of wild populations, affords an excellent opportunity to compare wild and weedy populations and to test hypotheses of weed origins and adaptation.
Although genetic differences between agricultural weeds and their wild relatives have been documented, closely related weedy and wild populations have rarely been compared (Ellstrand et al., 2010).
Identifying the genetic processes involved in the evolution of agricultural weeds from wild relatives requires identifying progenitor populations and characterizing genetic differences between progenitor and weedy populations (Guo et al., 2018). It may be difficult to trace the origin of a weedy population, particularly in outcrossing species that hybridize frequently and where weedy populations migrated far from their progenitor population. In addition, weedy populations at different locations may evolve similar or different traits, depending on local selection pressures, novel mutations, gene flow, drift, and the genetic backgrounds of progenitor populations (Délye, Menchari, et al., 2013;Ghanizadeh et al., 2019;Huang et al., 2017;Vigueira et al., 2013). Weedy giant ragweed is characterized by substantial morphological and genetic variability within and among populations, including the agriculturally important adaptations of herbicide tolerance, as well as seedling emergence that is both delayed and prolonged relative to nearby wild populations (Hovick et al., 2018;Patzoldt & Tranel, 2002;Schutte et al., 2008). Given the strong selective pressures that herbicide application and early-season weed management impose on weed populations, such traits are highly likely to be adaptive in agricultural fields but not in wild populations, thus contributing to these observed differences. Comparative analysis of weedy giant ragweed populations from multiple, distant locations enables testing for whether these, and other trait-based signatures of weediness, have evolved from a single, or multiple, independent location(s) and whether adaptation to agricultural fields involves a common set of genes (Stewart et al., 2009).
While novel mutations in coding sequences can have adaptive properties, gene expression variability may also contribute to rapid adaptation in new environments, enabling population establishment and the postestablishment evolution of adaptive traits (Charbonneau et al., 2018;Fraser, 2013;Lai et al., 2008;Mayrose et al., 2011;Vigueira et al., 2013). In fact, many genes are expressed differentially by weeds and their wild relatives, suggesting gene expression reprogramming may be critical for this evolutionary process (Lai et al., 2008;Leslie & Baucom, 2014;Mayrose et al., 2011;Xu et al., 2016). For example, gene expression changes have been observed in biotic and abiotic stimulus-response and stress-related protein genes in weedy compared with wild sunflower populations (Lai et al., 2008;Mayrose et al., 2011) and in response to herbicide application in morning glory (Ipomoea purpurea ;Josephs et al., 2021). Investigating differential gene expression between giant ragweed populations may help identify potential mechanisms by which this transition from wild to weedy occurred.
To understand the origins of agricultural weedy populations of giant ragweed (which lacks a sequenced genome) and the genetics distinguishing weedy from wild populations, we conducted RNAsequencing for 67 samples collected from 20 giant ragweed populations growing across the east-central U.S. Corn Belt, either in nonagricultural wild habitats or in crop fields (weedy populations).
We studied population structure, identified the most closely related wild populations to each of our weedy populations, and uncovered gene expression variation that distinguished weedy from wild populations. Our study suggests weedy giant ragweed populations arose via multiple independent origination events from local wild populations, followed by the spread of weedy populations regionally across crop fields. Differentially expressed genes, especially those involved in seed germination, vegetative stage change, and abiotic stress responses, may contribute to weediness in giant ragweed. Our results support the hypothesis that gene expression variability plays a key role in the convergent evolution of weedy plants. Within each region, we collected four weedy populations from agricultural (corn or soybean) fields and six wild populations from nonagricultural habitats (e.g., riverbanks and fencerows). Among them, samples from four pairs of weedy and wild populations were collected as paired adjacent populations sourced from the same geographic location (i.e., within 100 meters of each other).

| Sampling strategy
Otherwise, all populations were at least 2.7 kilometers apart. All populations were named with their state abbreviation (OH, IA, or MN), numeric order according to longitude from east to west, habitat (A for weedy populations in agricultural fields and W for wild, nonagricultural populations), and finally each individual sample was designated with a unique trailing numeral, for example, OH1-A-1 (Table S1).

| Reference transcriptome assembly and evaluation
We established a comprehensive pipeline for transcriptome construction, variant calling, and differential expression analysis with our samples ( Figure S1). We randomly selected a wild giant ragweed sample from an OH wild population (OH6-W-3) and generated 47 M paired-end reads to build a giant ragweed reference transcriptome. Raw read quality was evaluated with FastQC (Andrews, 2010), trimmed by Trimmomatic (Bolger et al., 2014), and de novo assembled with Trinity v2.9.0 using default parameters (Grabherr et al., 2011). We evaluated the assembled transcript quality using several methods: (1) We first mapped RNA-seq data onto the reference transcripts using Bowtie 2 (Langmead & Salzberg, 2012) (Fu et al., 2012), predicted transcript coding regions with TransDecoder v5.5.0 (Haas et al., 2013), and functionally annotated the predicted protein sequences with InterProScan (Jones et al., 2014). We also quantified transcript expression, defining transcript ≥1 TPM (transcript per kilobase per million reads) as expression support. In the end, all assembled transcripts lacking either predicted protein domains or expression support were removed to establish a filtered transcriptome.

| RNA-seq-based variant calling
To call single-nucleotide polymorphisms (SNP), we combined GATK RNA-seq best practices with Joint genotyping (Brouard et al., 2019;Van der Auwera et al., 2013). RNA-seq data from all 67 ragweed samples were mapped onto the filtered reference transcriptome with STAR in a two-pass model (--twopassMode Basic) (Dobin et al., 2013). Then, GATK4 was used to call variants for each sample according to RNA-seq best practice. To remove false SNPs, we compared SNP calls between two biological replicates (RNA extracted from the same plant), removing SNPs not found in both samples. This final step also removed putative tri-allelic variants resulting from paralog misalignment.

| Population genetic diversity and genetic structure analyses
Geographic distances between each pair of populations were calculated using latitudes and longitudes. Fixation indices (F ST ) among populations were calculated using VCFtools (Danecek et al., 2011) with -weir-fst-pop parameters (Weir & Cockerham, 1984 we calculated and compared cross-validation error to choose the best ancestral population inference. PCoA analysis was performed with PLINK 1.9 (Purcell et al., 2007). FastTree was used to build approximately maximum-likelihood phylogenetic trees (Price et al., 2010). Trees were displayed and modified with iTOL (Letunic & Bork, 2019). To check that inferences were not driven by SNPs under selection, we also built trees using SNPs that occurred in third-codon positions and were synonymous for amino acid identity; results were qualitatively the same, so we present analyses using the full dataset.

| Gene expression diversity between weedy and wild populations
RNA-seq data were mapped onto our reference transcriptome by HiSAT2 (Kim et al., 2019). Normalized expression values (transcripts per kb per million, or TPM) were calculated with StringTie (Pertea et al., 2015). We calculated the coefficient of variation (CV) for transcript abundance in each population to represent gene expression diversity (GED; Bellucci et al., 2014), which we interpreted as an indicator of genetic variability. We first compared GED between all weedy versus all wild samples using Kruskal-Wallis tests, doing so separately for OH and IA_MN populations.
We extracted the transcript IDs under each shifted peak region and conducted GO annotation for functional inference. Then, all eight weedy populations were paired with the most genetically similar wild population and population-level GED was compared. We equalized sample sizes for these weedy-wild pairs by randomly downsizing larger populations before calculating GED.
We extracted transcript IDs that had more than twofold difference in GED (CV values) in weedy populations compared with wild populations and conducted functional annotation on these gene groups. GO enrichment analysis was done with ClusterProfiler (Yu et al., 2012), and the adjusted p-value cutoff was .05 (Benjamini-Hochberg method).

| Differential gene expression analysis
We conducted differential gene expression analysis by estimating raw read counts for each gene by HTSeq based on mapping results (Anders et al., 2015). We identified differentially expressed genes between paired weedy and wild populations using DESeq2 (Love et al., 2014). To functionally annotate differentially expressed genes, we aligned our giant ragweed protein sequences onto the Uniprot dataset and then linked the Gene Ontology (GO) term and function description to aligned giant ragweed proteins to create a giant ragweed GO annotation file. Differentially expressed genes were subjected to functional enrichment analysis using ClusterProfiler.

| De novo assembly of unmapped reads from each population
Our giant ragweed reference transcriptome was established using one wild sample (

| Co-expression gene network analysis
Two independent co-expression gene networks were established for weedy and wild populations. We filtered out transcripts with low expression (TPM ≤ 1 in >80% of all samples). Then, gene networks were constructed using WGCNA (Langfelder & Horvath, 2008

| RNA sequencing, transcriptome assembly, and variant calling
Sixty-seven RNA-seq datasets were generated from 20 populations ( Figure 1a and Table S1), and one deeply sequenced sample was used to establish a giant ragweed reference transcriptome, composed of 41,669 trinity genes or 91,296 transcripts (Table S2). BUSCO assessment showed coverage of 91.3% conserved eukaryote ortholog genes with only 89 missing gene models ( Figure S2). About 57.2% of transcripts had protein homologs in the Uniprot database. Overall, 616,607 SNPs from the 67 giant ragweed transcriptomes were identified and used for downstream analyses (Table S3).

| Population analyses suggest independent origins with local spreading of weedy giant ragweed populations
Based on SNP data, we estimated the overall transcriptome nucleotide diversity (π), averaged across all giant ragweed populations, to be 0.0024. Nucleotide diversity did not differ between weedy and wild populations (Wilcoxon signed-rank test, p = .77; Table S4).
Observed heterozygosity (H o ) was lower than expected heterozygosity (H e ) for all but one population (Table S4). We tested the hypothesis of isolation by distance (IBD) among populations. The fixation index (F ST ) among all population pairs was low (F ST range = 0.002-0.109), suggesting that our giant ragweed populations were not greatly differentiated overall (Table S5)

| Gene expression diversity and differential gene expression between weedy and wild populations
We next focused on understanding gene expression differences between weedy and wild populations. We observed high reproducibility for our RNA-seq data (R 2 = 0.98 for two OH5-W biological replicates; Figure S4a) and high pairwise correlations (Pearson r > .9) for expression patterns among all samples, with the highest similarity among samples from the same population ( Figure S4b).

| Unique transcripts in weedy populations may relate to adaptive traits
To fully capture the identities of differentially expressed genes, we developed a novel pipeline to recover unique transcripts for each weedy-wild population pair that could not be mapped to our reference transcriptome (see Section 2 and Figure 5a). For samples from the four OH weedy populations, RNA-seq read mapping rates were between 69.75% and 80.09%, leaving ~0.4-2 M reads per sample for this de novo assembly (Table S6). From these four populations, we identified between 8537 and 23,492 transcripts present only in weedy populations and absent from the paired wild populations (Table S6). We applied an orthologous gene analysis approach on these unique transcripts and found 477 homologous gene groups (2317 genes) from all OH weedy populations ( Figure S8). Functional annotation revealed these shared genes fell into 260 different functional categories (Table S7 and Figure S8). For IA-MN weedy populations, we identified 4510 to 24,639 unique transcripts (Table S6) and 252 shared homologous gene groups (1301 genes), assigned to 163 functional groups ( Figure S8). Further comparison of functional groupings revealed 98 groups (GO terms) shared among all OH and IA-MN weedy populations (Figure 5b and Figure S8). These sets of unique transcripts indicate specific ways in which differential gene expression (inferred as the presence of unique transcripts) may contribute to the independent evolution of weediness across sites.
We repeated the above analysis with wild populations, completing de novo assembly using unmapped transcripts present only in our six wild populations and not in their paired weedy populations (Table S8) We further performed overlap detection with all weedy and wild functional groups and identified 10 weedy-specific pathways, including seed germination, rhythmic process, vegetative phase change, which relates to seedling emergence timing, and pathways involved in nitrogen uptake and assimilation (urea cycle and glutamine biosynthesis;

| Gene networks between weedy and wild populations
We established independent co-expression gene networks for weedy and wild samples by grouping together transcripts with similar expression profiles into modules, resulting in networks of 45 and 27 gene modules, respectively (Section 2 and Figure S10a). We then as-

| Herbicide resistance gene identification
We tested for constitutive differential expression of transcripts for eight major herbicide-target genes (Figure 6a) between weedy and wild samples to determine whether constitutive overexpression of these genes may contribute to weediness in giant ragweed. We did not detect significant differences in the herbicide resistance genes' expression between weedy and wild populations (Figure 6b), but the ALS gene did have borderline significant overexpression in weedy compared with wild samples (p = .052); thus, changes to the regulation or copy number of this gene in weedy individuals may have occurred.
Besides gene overexpression, changes in herbicide-target gene coding sequences could also affect herbicide resistance. Several key mutations in genes coding for proteins targeted by herbicides have been identified that confer herbicide resistance (Tranel et al., 2018).
Prior to this point, just one such mutation in ALS has been identified in both giant and common ragweed (a Trp-574-Leu substitution), which confers resistance to ALS inhibitors (Marion et al., 2017;Tranel et al., 2018). In our dataset, we characterized 71 SNPs across ALS transcripts and found six that caused nonconservative amino acid replacement at four loci (Figure 6c,d), including three ALS inhibitor resistance mutations that have been previously confirmed in multiple species (Pro-197-Ser, Pro-197-Leu, and Trp-574-Leu;Heap, 2021). We further   and Trp-574-Leu (P1897)) (highlighted green) and two potential resistant mutations (Arg-26-Leu (P304) and Ser-41-Phe (P349)) (highlighted gold). The SNPs, codon change, and amino acid substitution are shown here. For example, P1897 represents a mutation from G to T at nucleotide position 1897 in the giant ragweed ALS transcript, which alters the codon from TGG to TTG, and subsequently replaces Tryptophan with Leucine at that location. Mutations P765 and P766 within one codon could cause three different kinds of amino acid replacements. Sample counts with different genotypes at these four positions are listed (0/0, reference homozygous; 0/1, heterozygous; and 1/1, alternative homozygous).
nine segregating SNPs were found at the previously confirmed ALS inhibitor resistance loci (Figure 6d). Population genotyping analyses confirmed this observation, revealing that across 67 samples, two weedy and three wild samples had the Trp-574-Leu-resistant genotype. Three weedy and one wild sample had the Pro-197-Ser substitution, one wild sample had the Pro-197-Leu substitution, and two weedy and three wild samples had the Pro-197-Phe substitution, implying ALS inhibitorresistant genotypes are not exclusive to the weedy populations we sampled ( Figure 6d and Table S10).
For other herbicide-target genes without experimentally confirmed herbicide resistance mutations in giant ragweed, we characterized all SNPs and identified polymorphic sites that significantly segregated between weedy and wild samples ( Figure S11 and Table S11), providing a starting point for future studies to screen for novel mutations leading to herbicide resistance.

| Giant ragweed transcriptome variability
We observed low nucleotide diversity among giant ragweed populations and found weedy and wild populations to be genetically similar within a given region, suggesting high gene flow across the sampled area. These results are consistent with giant ragweed's windpollination mating system, in which outcrossing is expected to lead to high diversity within, but low diversity among, populations (Hovick et al., 2018;Leon et al., 2021;Radosevich et al., 2007). Limited genetic variation among populations has also been observed in other outcrossing native weeds, including common ragweed, sunflower, and water hemp (Hämälä et al., 2020;Lai et al., 2008;McGoey & Stinchcombe, 2021;Waselkov et al., 2020).
The use of transcriptome sequences in lieu of genome-wide sequences may have contributed to the low among-population diversity we observed, since coding regions are generally more conserved and under greater selection pressure (Jehl et al., 2021;Makałowski & Boguski, 1998;Zhao et al., 2003); however, transcriptome data are commonly used to determine population structure of species like giant ragweed that lack a reference genome sequence or those with structurally complex genomes, and they provide added insights into transcript expression diversity (Hämälä et al., 2020;Okada et al., 2018;Ophir et al., 2014;Takahagi et al., 2016).

| Multiple origins followed by local spread
Weedy populations derived from wild populations may originate from multiple, local independent origins or from a single origin with subsequent spreading of adapted biotypes across the landscape (Basu et al., 2004;Charbonneau et al., 2018;Ellstrand et al., 2010;Vigueira et al., 2013;Waselkov et al., 2020). Our data suggest that these pathways work simultaneously to contribute to the origins of weedy giant ragweed populations. Population structure and phylogenetic analyses both suggest that weedy populations from OH and IA-MN originated independently, since weedy populations showed more genetic similarity with nearby wild populations than with distant weedy populations (Figure 1). The hypothesis that weediness can evolve independently across multiple regions is also supported by recent reports of independent origins for herbicide resistance across weedy giant ragweed populations  and the evolution of weedy sunflower biotypes Lai et al., 2008).
We can also infer widespread dispersal among weedy populations, based on clusters on the phylogeny including individuals from geographically distant weedy populations (e.g., OH4-A and OH7-A; to third-party operators who harvest from multiple farms (Chauvel et al., 2021;Vink et al., 2012).
Giant ragweed is monoecious, wind-pollinated, and a producer of copious pollen, which may facilitate recurrent outcrossing between adjacent weedy and wild giant ragweed populations, (Bassett & Crompton, 1982), leading to gene flow and complicating the identification of population ancestry (Charbonneau et al., 2018;. Pollen-mediated gene flow in giant ragweed has been observed at a rate of 3%-4% at 50 m from the pollen source (Ganie & Jhala, 2017) and over 30% at distances less than 0.76 m (Brabham et al., 2011;Ganie & Jhala, 2017). These results, and our own findings, suggest weedy giant ragweed populations can originate independently from wild populations, and once established, may then spread across fields and/or pass on weediness genes to wild populations through outcrossing.

| Gene expression diversity
Gene expression regulation has been proposed to play a key role in the evolution of weeds (Charbonneau et al., 2018;Josephs et al., 2021;Lai et al., 2008;Vigueira et al., 2013). We found greater GED overall in wild versus weedy giant ragweed populations, possibly reflecting its evolution in riparian habitats subjected to frequent disturbance (Bassett & Crompton, 1982;Waselkov et al., 2020).
Such a pattern could also result if genetic bottlenecks occur as wild plants invade crop fields, reducing population phenotypic and genetic variance.
Despite higher overall GED in wild giant ragweed populations, we identified individual genes for which GED was higher in weedy populations than in their paired wild populations (and vice versa).
The annotated pathways of these two groups of genes showed considerable overlap, suggesting that the evolution of weediness may involve increases or decreases in expression diversity at the level of individual genes within a pathway. Future efforts to determine how such variation in GED may contribute to fitness differences at the population level would be worthwhile.

| Differential gene expression
Using the conventional DESeq2 approach, we identified relatively few DEGs and thus relatively little differentiation in gene expression among weedy and wild populations. The differentiation we did identify was mostly population-specific, supporting the hypothesis of multiple, independent origins of weedy populations through different genetic means. These results are consistent with genomic and gene expression studies of weedy and wild sunflower populations in the U.S. (Bock et al., 2020;Drummond, 2018;Lai et al., 2008), indicating that this pattern is not unique to our study system.
A shortcoming of the conventional DESeq2 approach in our study was using one wild giant ragweed transcriptome as the reference for mapping transcriptome reads from all other samples; this resulted in loss of transcripts that may be uniquely present or expressed in other giant ragweed weedy and wild transcriptomes.
We rectified this issue by implementing a novel pipeline to retrieve and analyze both weedy and wild unmapped reads, identifying thousands of transcripts common to all our weedy populations and providing evidence for potential convergence among weedy populations and divergence from wild populations. Weedy traits in independently evolved weedy populations may thus arise through two sets of evolutionary mechanisms, one reflecting convergent evolution in response to common selection pressures (Huang et al., 2017;Thurber et al., 2013;Vigueira et al., 2013), and the other reflecting population-specific evolutionary change shaped by the original genetic background, genetic drift, random mutations, and locationspecific selection pressures.
Many DEGs common across our weedy giant ragweed populations were involved in functional pathways presumably important for adapting to agricultural fields, such as seed germination, rhythm responses, vegetative phase change, and nitrogen assimilation. Seed germination, rhythm responses, and vegetative phase change functional pathways directly relate to prolonged seedling emergence timing characteristic of weedy giant ragweed. Common garden studies have shown clear phenotypic differences between weedy and wild giant ragweed in the duration of seedling emergence and onset of flowering (Hartnett et al., 1987;Schutte et al., 2008Schutte et al., , 2012Sprague et al., 2004), presumably because prolonged seedling emergence avoids some mortality from early-season weed management practices and earlier flowering may be adaptive in ensuring reproduction before crop harvest. Geographic variation in agriculturally adaptive traits has been reported for this and other native weeds (Bravo et al., 2017;Waselkov et al., 2020) and may reflect regional variation in agrestal selection histories. In sunflowers, downregulation of defense genes in resource-rich environments such as crop fields may liberate resources that enable increased competitiveness and fitness (Mayrose et al., 2011). We note that the transcript differences we detected in our study were found in leaf tissues harvested from seedlings grown under identical greenhouse conditions and thus are constitutive changes and do not simply reflect responses to environmental conditions. Future studies investigating gene expression in weedy versus wild giant ragweed across multiple tissue types and under varying environmental conditions will provide additional insight into the genes we identified here, perhaps detecting additional candidate "weediness genes."

| Altered gene co-expression pattern
The rewiring of gene co-expression networks in cultivated species compared with their wild relatives has recently been hypothesized (Fajardo & Quecini, 2021;Jones & Vandepoele, 2020). Analogously, altered gene expression patterns in the transition to becoming weedy could result in variable co-expression networks. Our analyses revealed eight such variable gene modules between weedy and wild giant ragweed populations, two of which are associated with herbicide resistance. One of these variable modules is involved in branched-chain amino acid biosynthesis, an essential biochemical pathway targeted by some herbicides (Marion et al., 2017;Vila-Aiub et al., 2009), and the other functions in xenobiotic transport, which is connected to herbicide resistance via detoxification of herbicides and other harmful compounds (Gaines et al., 2020). These gene expression network differences may reflect large-scale evolutionary responses to the strong selection pressures exerted by repeated herbicide use in agricultural fields.

| Herbicide resistance genes
Herbicide resistance is often implicated as a major facilitator of the rapid expansion of weedy biotypes in agricultural fields across species (Baucom, 2019;Délye, Jasieniuk, & Le Corre, 2013;Harre et al., 2017;Heap, 2021;Vink et al., 2012). We hypothesized that herbicide resistance contributes to the increased survival of weedy giant ragweed in agricultural fields and tested whether there was a correlation between weediness in giant ragweed and increased expression of major herbicide-target genes, either due to changes in gene regulation or increased gene copy number (Gaines et al., 2010). Based on the similar expression levels of seven major herbicide-target genes across weedy and wild populations, constitutive overexpression of these genes is not a significant contributor to weediness in giant ragweed (see also Moretti et al., 2018).
Our study investigated the constitutive expression of these herbicide resistance genes under normal, untreated conditions, so additional studies are needed to determine whether these genes are overexpressed in weedy populations when exposed to the herbicides in question. We did find marginally significant overexpression of ALS gene transcripts in weedy compared with wild individuals (p = .052), suggesting constitutive overexpression of ALS may provide a survival advantage to weedy individuals in agriculture fields. Future genomic analyses of weedy and wild individuals will allow for the identification of genetic variation in regulatory regions contributing to this differential expression.
We also investigated SNP variation within herbicide resistance gene transcript sequences between weedy and wild giant ragweed individuals, which could contribute to resistance phenotypes.
However, our investigation of ALS transcript sequences suggested that point mutations previously confirmed to confer resistance to ALS-inhibiting herbicides were not the only factors driving the evolution of weediness in our study populations, since they occurred in both weedy and wild populations. Of course, the presence of herbicide-resistant genotypes in wild populations could reflect the transmission of point mutations from resistant weedy individuals through pollen flow, but standing variation in wild giant ragweed populations that remains in the genome neutrally or is selected for directly via herbicide application to field margins and rights-of-way could also contribute (Drummond, 2018;Preston & Powles, 2002). Patzoldt and Tranel (2002) identified at least 15 different ALS alleles in a weedy giant ragweed population resistant to ALS inhibitors after only 3 years of herbicide selection pressure, and a high frequency of alleles conferred herbicide resistance (0.25). We do not know the herbicide application histories of our study populations, nor if our weedy population individuals were resistant to ALS-inhibitor herbicides, but within-population frequencies of ALS-resistant genotypes frequencies were similarly high (up to 0.33; Table S10).
Although this suggests past applications of ALS-inhibitor herbicides may have influenced the evolution of even our wild populations, future herbicide treatment experiments, combined with genotyping analyses of ALS and other herbicide-target genes in weedy and wild giant ragweed, are needed to clarify what role herbicide resistance plays in the evolution of weedy giant ragweed.

| Weediness evolution in outcrossing native species
Recent surges in native, outcrossing species that have become weedy in North America indicate that some species are well positioned to become weedy or invasive in their native environment (Dekker, 2016;Leon et al., 2021;Waselkov et al., 2020). The diversity of adaptive traits such species exhibit, including resistance to multiple herbicides, nontarget site resistance, and morphological/phenological shifts, suggests these transitions often reflect complex genetic changes that may involve multiple genes, epistatic interactions, gene expression plasticity, and/or pleiotropy (Drummond, 2018;Hämälä et al., 2020;Leon et al., 2021;McGoey & Stinchcombe, 2021). The combination of large population sizes and substantial population genetic diversity in these species, including giant ragweed, likely enhances this genetic complexity and contributes to their transformation into problematic weeds. Intensive agriculture in North America is a relatively recent phenomenon, imposing strong and homogenous selection pressures over enormous geographic scales. This may help explain why native outcrossing species have emerged only recently as major agricultural weeds in North America and suggests that we can expect continued evolution of new weedy species in the future.

DATA AVA I L A B I L I T Y S TAT E M E N T
The raw data used in this study have been deposited on NCBI under the BioProject (PRJNA726544).